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Getting Started

This page provides basic tutorials about the usage of mmdetection. For installation instructions, please see INSTALL.md.

Due to the optimization and supplementary experiments during the review process, we are sorting and optimizing the code and will update it as soon as possible.

Prepare DOTA dataset.

It is recommended to symlink the dataset root to AerialDetection/data.

Here, we give an example for single scale data preparation of DOTA-v1.0.

First, make sure your initial data are in the following structure.

data/dota
├── train
│   ├──images
│   └── labelTxt
├── val
│   ├── images
│   └── labelTxt
└── test
    └── images

Split the original images and create COCO format json.

python DOTA_devkit/prepare_dota1.py --srcpath path_to_dota --dstpath path_to_split_1024

Then you will get data in the following structure

dota1_1024
├── test1024
│   ├── DOTA_test1024.json
│   └── images
└── trainval1024
     ├── DOTA_trainval1024.json
     └── images

For data preparation with data augmentation, refer to "DOTA_devkit/prepare_dota1_aug.py"

For data preparation of dota1.5, refer to "DOTA_devkit/prepare_dota1_5.py" and "DOTA_devkit/prepare_dota1_5_aug.py"

Inference with pretrained models

Test a dataset

  • single GPU testing
  • multiple GPU testing

You can use the following commands to test a dataset.

# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}]

Optional arguments:

  • RESULT_FILE: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.

Examples:

Assume that you have already downloaded the checkpoints to work_dirs/.

  1. Test DEA.
python tools/test.py configs/DOTA/faster_rcnn_RoITrans_r101_fpn_dea.py \
    work_dirs/faster_rcnn_RoITrans_r101_fpn_dea/epoch_12.pth \ 
    --out work_dirs/faster_rcnn_RoITrans_r101_fpn_dea/results.pkl
  1. Parse the results.pkl to the format needed for DOTA evaluation

For methods with only OBB Head, set the type OBB.

python tools/parse_results.py --config configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota.py --type OBB

Demo of inference in a large size image.

python demo_large_image.py

Train a model

mmdetection implements distributed training and non-distributed training, which uses MMDistributedDataParallel and MMDataParallel respectively.

All outputs (log files and checkpoints) will be saved to the working directory, which is specified by work_dir in the config file.

Train with a single GPU

python tools/train.py ${CONFIG_FILE}

If you want to specify the working directory in the command, you can add an argument --work_dir ${YOUR_WORK_DIR}.

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

Optional arguments are:

  • --validate (recommended): Perform evaluation at every k (default=1) epochs during the training.
  • --work_dir ${WORK_DIR}: Override the working directory specified in the config file.
  • --resume_from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.